La conciliation médicamenteuse : perception du pharmacien d'officine en France et au Québec
Bibliographic record
Abstract
During my 5th year internship at the University Hospital in Quebec from June to September 2019, a study was conducted to analyze the perception of city pharmacists regarding medication reconciliation at discharge. The study took place in 3 general hospitals in Montreal (Fleury Hospital, Jean-Talon Hospital and Hôpital du Sacré-Coeur-de-Montréal) from July 11 to 25, 2019. One year later, a similar study was conducted in France, at the Centre Hospitalier Charles Perrens in Bordeaux, from July 20, 2020 to September 20, 2020. The objective was to compare the drug reconciliation activity between the two territories and to explore areas for improvement in order to optimize this practice. To carry out the study, two distinct questionnaires, adapted to each of the health systems, were developed. The Quebec questionnaire consisted of 9 questions and the French questionnaire consisted of 11 questions. Each questionnaire consisted of open-ended and multiple-choice questions. Fifty responses were studied in Quebec, compared to 23 in France. In Quebec, medication reconciliation is a process that is rooted in practice. On the other hand, the level of knowledge in France remains very heterogeneous, with 22% of the dispensing pharmacists surveyed not being familiar with this practice. In addition, a second difference is noticeable in the tools used to transfer information (Dossier Santé Québec, Secure Messaging, Fax, Pharmaceutical File, Shared Medical File, etc.). This work made it possible to show the importance and necessity of an optimal information system to be able to carry out the activity of drug reconciliation in the best possible way and thus strengthen the city-hospital link.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".